For a more direct comparison, we also train a more expressive model than the simple MTL-based model we propose. This architecture is based on bi-directional LSTMs [1]}. For this model, we input sequences of embedded words (using pre-trained word embeddings) from each query into independent BiLSTM blocks and output a vector representation for each query. We then concatenate the vector representations with the similarity features from our MTL model and feed it into a dense layer and a classification layer. This way we can evaluate the usefulness of the flexible, expressive LSTM network directly (as our MTL model becomes an ablation instance of the full, more complex architecture). We use the same dropout regularization and SGD values as for the MLP. Tuning all parameters on the development data, we do not manage to outperform our proposed model, however. See lines MTL-LSTM-SIM in Table 1 for results.
